Traditionally, scanning sensors are applied in sheet forming processes to measure sheet property variability. Unfortunately, while the sensors scan across a forming sheet in the cross-machine direction (CD), the sheet is very rapidly moving past the scanning sensors in the machine direction (MD). The scanning sensors, thus, actually measure only a zigzag path on the two-dimensional sheet being formed. Using this scanning technique, there is no easy way to completely separate the real sheet variations occurring in the machine direction from the real sheet variations occurring in the cross-machine direction. In addition, since the sensors measure only along a zigzag path scanned on a two-dimensional sheet, the majority of the sheet is not measured at all. With these inherent limitations of a scanning mechanism, the measurements obtained from a scanning sensor are very sparse and can only be used to roughly approximate the real sheet MD, CD, and residual variations. Any further analysis of sheet variations based on a scanning measurement is severely limited by the fact that the raw measurement itself is not an easily separable two-dimensional measurement.
Recently, non-scanning, full-sheet measurement has become commercially available to measure the entire sheet width without movement of sensors back and forth across the sheet and without missing any portions of the sheet, see U.S. Pat. No. 5,563,809 which is incorporated herein by reference. Such measurements can be taken at many locations along a sheet-forming process. Using these measuring techniques, a massive amount of full-width, truly two-dimensional (2D) measurement data is available almost continuously. These measurements contain profound information about sheet variations which have not been observed with conventional scanning techniques used before.
To fully utilize all information contained in these two-dimensional measurements, the two-dimensional measurement data cannot be processed as in the past. Accordingly, there is a need for improved and novel processing techniques which can extract and classify useful information about sheet variations so that persons using the measurement equipment can readily recognize different types of variations and identify the causes of the variations in the process which is manufacturing the sheet being measured.
This need is met by the invention of the present application wherein multiple two-dimensional variation patterns are extracted from two-dimensional sheet measurement data of a sheet of material taken as the sheet is being manufactured and classified to identify the causes of the extracted patterns. The extracted two-dimensional variation patterns are identified with the elements in the process which caused the patterns, i.e., components of the machine making the sheet. The elements of the process which cause the patterns can then be adjusted and/or controlled so that the patterns can be reduced or substantially eliminated in sheets of material produced by the process. In addition to adjustment and/or control of the process or machine producing the sheet of material, the extracted variation patterns can be used as new representations of sheet quality, process or machine quality and the patterns will provide more in-depth understanding for operators of the machine.